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The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the interactions of natural and anthropogenic variables at different spatial scales related to changes in mangrove distribution during a relatively wet period (1972-1990) and a dry period (1991-2004) in subtropical eastern Australia. Previous research has demonstrated that mangroves are encroaching into salt marsh. Mangrove spatial change in southeast Queensland is related generally to landscape variables especially during the relatively wet period. What has not been explored is the spatial scale of the influence under the two rainfall regimes (wet and dry) and that is the aim of this paper. Ten sites were examined at different levels of resolution including catchment, sub-catchment and two buffer zones (1000 and 500 m), under the period of relatively higher and lower rainfall. Land use was ascertained from Landsat satellite imagery using Maximum Likelihood Classification techniques. Partial least squares regression analysis was used to study the relationships between the predictor variables and the rate of change in the mangrove distribution. The research has found that the impact of land use/cover on the encroachment of mangrove into saltmarsh can vary and appears to be related to rainfall patterns, which in turn affect hydrological connectivity. A major finding of this research was that the changing spatial patterns of mangroves during the wet period was more a function of land use/cover pattern and population density at the sub-catchment level, whereas during drier periods it was more affected by the local effects of nearby land use/cover in buffer zones. [ABSTRACT FROM AUTHOR]